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Soren Archibald

成为会员时间：2022

青铜联赛

2600 积分
Preparing for your Professional Data Engineer Journey Earned Oct 5, 2022 EDT
利用 BigQuery ML 构建预测模型时的数据工程处理 Earned Sep 30, 2022 EDT
Serverless Data Processing with Dataflow: Develop Pipelines Earned Sep 29, 2022 EDT
Serverless Data Processing with Dataflow: Operations Earned Sep 28, 2022 EDT
在 Compute Engine 上实现负载均衡 Earned Sep 28, 2022 EDT
在 Google Cloud 上为机器学习 API 准备数据 Earned Sep 25, 2022 EDT
Build Streaming Data Pipelines on Google Cloud Earned Sep 23, 2022 EDT
Serverless Data Processing with Dataflow: Foundations Earned Sep 15, 2022 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Sep 15, 2022 EDT
Build Batch Data Pipelines on Google Cloud Earned Sep 13, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Sep 12, 2022 EDT
How Google Does Machine Learning Earned Sep 2, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Aug 31, 2022 EDT

This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

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完成中级技能徽章课程利用 BigQuery ML 构建预测模型时的数据工程处理， 展示自己在以下方面的技能：利用 Dataprep by Trifacta 构建 BigQuery 数据转换流水线； 利用 Cloud Storage、Dataflow 和 BigQuery 构建提取、转换和加载 (ETL) 工作流； 以及利用 BigQuery ML 构建机器学习模型。

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In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.

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In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.

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完成入门级在 Compute Engine 上实现负载均衡技能徽章课程，展示自己在以下方面的技能： 编写 gcloud 命令和使用 Cloud Shell，在 Compute Engine 中创建和部署虚拟机， 以及配置网络和 HTTP 负载均衡器。 技能徽章是由 Google Cloud 颁发的专属数字徽章， 旨在认可您在 Google Cloud 产品与服务方面的熟练度； 该课程会检验您在交互式实操环境中运用所学知识的 能力。完成此技能徽章课程和作为最终评估的实验室挑战赛， 即可获得技能徽章，并在您的圈子中秀一秀。

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完成入门级技能徽章课程在 Google Cloud 上为机器学习 API 准备数据，展示以下技能： 使用 Dataprep by Trifacta 清理数据、在 Dataflow 中运行数据流水线、在 Dataproc 中创建集群和运行 Apache Spark 作业，以及调用机器学习 API，包括 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。

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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.

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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.

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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.

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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting. Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.

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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.

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This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models. The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.

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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

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